The Power of Analytics

We all know that analytics are a powerful tool, and in fact analytics have become an increasingly hot topic in the age of big data. We gather more and more data every day, but we are still trying to make heads or tails of that data. And analytics is not a new topic: Google Analytics has been around since late 2005 and Google certainly was not the first company to float the concept of being able to make more valuable decisions based on data insights. There is a wide range of companies today offering analytics services, from data visualization specialists Tableau to Microsoft with Power BI and Salesforce with its Wave platform.

At PROS, we see analytics as providing huge benefits to our customers from two unique but intertwined dimensions, the quality of the analysis itself and the insights and prescriptive recommendations it offers to the business.

As analytics and predictive sales and pricing analytics software become more ubiquitous in today’s technology market, it’s important to understand the different steps in the analytics process. If your organization is considering investing in analytics, it is vital to know the capabilities – and limitations – of any analytics tools or processes you are exploring or intend to deploy. Below are the four main analytics steps that take you all the way from raw data to prescriptive business insights.

DescriptiveAnalytics
This step allows the business to produce reports and data visualizations in ways that can help it support day-to-day operations. One such example of descriptive data — using a car analogy — could simply be your speedometer or fuel gauge. These indicators allow the driver to better understand how the car is currently performing – and allow him/her to extrapolate on future needs.

PredictiveAnalytics
In this step, the business starts to learn what it can predict with this information. This is where many scientific disciplines come into play – from statistics to physics – to better leverage the information at hand. What can we predict from the knowledge that the fuel gauge is low? What is the expected range of the car before needing to refuel?

Prescriptive Analytics
Now that the business is able to observe behaviors and leverage data to forecast future behaviors, what can it do to proactively adjust and improve the outcome of our work? In the case of the car analogy, such a prescriptive step might be suggesting that the driver re-route in order to include an appropriately timed fuel stop on his trip from origin to destination.

Automation
For the final step, let’s use the car analogy one final time. Some automobile GPS systems attempt to offer a degree of automation by re-routing users in real-time, often with limited success. This is probably the ultimate goal of analytics: to automate well-understood decisions and remove the need for human interaction, while instead focusing users on areas that are not well understood and more uncertain. Some systems today already offer some level of automation based on analytics. Most notably, modern commercial jet aircraft include an auto-pilot feature that is designed to automatically adjust to changing conditions. Only when the autopilot is unsure how to respond do the pilots take over and assess the situation.

Beyond the four levels of analytics described above – descriptive, predictive, prescriptive and automated – there is one crucial additional aspect that we strive to develop at PROS: analytics availability. There are essentially two ways in which analytics can be delivered: inside of an application through visualization or table-like structures, or made available for the businesses via BI tools. It is very important to offer both capabilities to customers. This allows them to be presented with some baseline visualizations to get them started, but also enables them to build their own specific analytics capabilities. The latter is necessary because it is next to impossible to offer a “one size fits” all analytics solution to all customers. Even within the same industry, different customers have different ways of looking at the same data and may need to have it presented differently.